user
Tell me how would you respond to the following request.
Describe the similarities between decision trees and random forests.

assistant
Decision trees and random forests are both supervised machine learning models that are used for classification problems. Both models use a tree-like structure to make decisions. In a decision tree, each node is a decision point and the model descends down the tree until it reaches a leaf node, which is the prediction. A random forest is similar, but instead of a single decision tree, it includes multiple decision trees trained on slightly different data and combined together. The result is a better prediction model with improved accuracy and reduced overfitting.